Welcome to "Use of Data" in PE!
Numbers and graphs might seem like they belong in a Math lesson, but in Physical Education, data is your best friend. It is the evidence that proves you are getting fitter, faster, or stronger. In this chapter, we will learn how to collect, show, and understand information to help athletes reach their peak performance.
Don't worry if you aren't a "math person"—we are going to break this down into simple steps that make sense for any sport!
1. The Two Types of Data: Quantitative vs. Qualitative
When we "use data," we are basically looking at two different ways to describe what is happening in a sport.
Quantitative Data (Think "Quantity")
This is all about numbers. It is information that can be measured and written down with a value. It is "hard" evidence because it doesn't rely on opinions.
• Example: A 100m sprint time of 12.5 seconds.
• Example: A heart rate of 150 beats per minute (BPM).
• Example: Scoring 15 goals in a season.
Qualitative Data (Think "Quality")
This is about descriptions, feelings, and opinions. It explains the "how" and "why" behind a performance. It is subjective, meaning it depends on who is giving the feedback.
• Example: A coach saying, "Your footwork looked very graceful today."
• Example: An athlete saying, "I felt very tired and unmotivated during the second half."
• Example: Describing the technique used during a tennis serve.
Quick Review:
Quantitative = Measuring (Numbers/Facts)
Qualitative = Describing (Opinions/Feelings)
Memory Aid: Use the letters in the words to help you remember!
QuantiNtative has an 'N' for Numbers.
QualiLtative has an 'L' for Letters (or words).
Key Takeaway: To get the full picture of an athlete's performance, you need both types of data. The numbers tell you what happened, and the descriptions tell you how it felt or looked.
2. Presenting Your Data
Once you have collected your data (like your scores from a fitness test), you need to show it clearly. We usually do this using Tables and Graphs.
Tables
Tables are great for organizing raw data. They should always have clear headings and include the units of measurement (like seconds, cm, or kg).
Graphs and Charts
Graphs help us see patterns or "trends" (how things change) more easily than a list of numbers.
• Bar Charts: Best for comparing different categories. Example: Comparing the average heights of players in different positions on a netball team.
• Line Graphs: Best for showing how something changes over time. Example: Tracking your heart rate every minute during a 20-minute run.
• Pie Charts: Best for showing parts of a whole (percentages). Example: Showing what percentage of a football team's goals were scored with the left foot, right foot, or head.
Common Mistake to Avoid: When drawing or reading a graph, always check the axis labels! If you don't know what the bottom line (X-axis) or the side line (Y-axis) represents, the data won't make any sense.
Key Takeaway: Graphs turn boring numbers into a visual story. Use a line graph for "time" and a bar chart for "comparing groups."
3. Interpreting and Analysing Data
Interpreting data just means "explaining what the data shows." Analysing means "explaining why the data is important."
When you look at a graph or table, ask yourself these three questions:
1. What is the highest and lowest value?
2. Is there a trend? (Is the line going up, down, or staying flat?)
3. Are there any anomalies? (A result that is totally different from the others, like a sudden spike or drop).
The "So What?" Factor
If your data shows your resting heart rate has dropped from 70 BPM to 60 BPM over six weeks, the interpretation is that your heart rate is lower. The analysis is that your cardiovascular system has become more efficient due to training!
Key Takeaway: Don't just state the numbers; explain what they tell us about the athlete's fitness or performance.
4. Normative Data (The "Measuring Stick")
How do you know if a fitness test result is actually "good"? You compare it to Normative Data.
Normative Data refers to a set of "standard" results collected from a large group of people. It allows you to compare an individual's score against the "norm" for their age and gender.
• Example: If you do the Cooper 12-minute run and score 2200 meters, you look at a normative table to see if that is "Excellent," "Average," or "Poor" for someone your age.
Did you know? Normative data is usually broken down by age and gender because a 15-year-old boy and a 50-year-old woman are expected to have different physical capabilities.
Analysing your own results:
When you evaluate your own data against normative data, you can:
1. Identify strengths (where you scored in the "Excellent" category).
2. Identify weaknesses (where you scored in the "Poor" category).
3. Set targets for your next training block.
Key Takeaway: Normative data gives your results context. It tells you where you stand compared to everyone else.
Quick Review Box
1. Quantitative: Numbers and measurements (facts).
2. Qualitative: Descriptions and opinions (feelings).
3. Presentation: Use Line Graphs for time and Bar Charts for comparisons.
4. Interpretation: Look for the "story" in the data (trends and patterns).
5. Normative Data: Compare your scores to national averages to find strengths and weaknesses.
Don't worry if this seems tricky at first! Just remember: Data is simply a way of measuring progress. If you can read a scoreboard or a stopwatch, you are already using data!